You already know the odds are stacked against you.
Not because you pick the wrong horses.
Not because you lack knowledge or haven't put the effort in.
But because the machine you're betting against was built specifically to beat you before the starting gun fires.
The major bookmakers stopped pricing races by hand a long time ago.
They run sophisticated AI systems that process millions of data points, update odds in real time as money flows through their markets, and flag profitable accounts for restriction the moment a winning run gets going.
That's what you're sitting down against every morning with your phone and a cup of tea.
And for years, I was in the same position you're probably in right now.
Losing more than I won, not through bad luck, but because I was bringing the wrong tools to a very unequal fight.
Then I stopped trying to fight that machine with a pen, a spreadsheet and a gut feeling.
And spent 18 months building something that could actually compete with it on its own terms.
My name is Dan Wilson, and I'm going to show you what that something produces.
In May 2026, the betting app I built banked £5,960.40.
That's not a back-test figure or a best-case projection.
It's what landed in my betting accounts last month.
Before I explain how it works, let me show you what happened when I opened it up to a small group for the very first time.
“I’m a long-distance driver from Hull so the last thing I needed was something demanding hours of my morning. Bet Analyser App took me 4 minutes a day at most, and I finished the trial with £2,180 sitting in my account.”
Michael Gallagher, Huddersfield
“My brother wouldn’t stop going on about Dan’s selections so I eventually joined just to see for myself, fully expecting to walk away disappointed. Three weeks later I was £1,940 up, and he was asking me to put his bets on for him.”
Fiona Marsh, Woking
“I’ve tried services like this before and walked away with nothing to show for it, so I came in with rock-bottom expectations. The strike rate across my 30 days came out at 64% and I finished the trial £2,350 up, more than most people make from betting across an entire year.”
Gerry Thornton, Derbyshire
What do these 3 people have in common?
Not one of them had been making consistent profit from betting before joining.
They each had the same profile you'll probably recognise.
A strong love of the horses, a string of promising runs that never held together over time, and a growing conviction that something fundamental was missing from their approach.
That missing piece isn't more knowledge or more research.
It's a systematic, data-driven edge against a market that processes information faster than any human being can match.
With between £1,940 and £2,350 in their pockets from a single month of following the app's selections, they've finally found it.
And you can have access to exactly the same thing.
But first it's worth understanding why this has been so difficult to achieve on your own.
Most punters assume the solution is finding the right system, backing the right tipster, or simply putting more hours into the research.
None of those things address the real problem.
The real problem is structural, and it's been sitting underneath every bet you've ever placed.
The bookmakers don't compete with you on equal terms.
They run pricing algorithms built and maintained by teams of data scientists, updated continuously as money moves through their markets, and engineered to extract long-term margin across millions of bets per year.
Every horse in every race gets a price designed to be profitable for the house, not a price that accurately reflects each runner's true probability of winning.
The gap between the published odds and the runner's actual win probability is where all the real money in betting lives.
Finding that gap consistently, before the market closes it, is something no person working manually can do at the required speed and scale.
The data volume is too large for a human workflow.
The window before the price corrects itself is too short.
And the emotional detachment required to back a horse purely on probability rather than preference isn't something most people are built for.
None of this makes profitable betting impossible.
It makes profitable betting impossible with the wrong approach.
A properly built system, processing the same data the bookmakers use, finding mispricings at scale every morning before the first race goes off, changes the picture entirely.
That's the difference between finishing the month down and finishing it with a substantial withdrawal from your account.
That cutting edge is Bet Analyser App.
Think about the last 12 months of your betting.
How many times did you back a horse at 5/1 that the market had drifted out from 3/1 in the morning, and it won?
The drift told you the sharp money didn't agree with your assessment.
But the horse still won, which means the morning price was wrong, not the result.
That's a value gap playing out right in front of you.
Now imagine a system that finds those gaps every single morning, across every meeting, across every race on the card, and sends you the best 2 or 3 of them before 8am.
That's not a fantasy or a projection.
It's exactly what the 12-month results table further down this page shows you.
Here's something most punters don't fully appreciate about how the bookmakers actually operate.
They don't just price races from the same form data you can read for free every morning.
Their systems monitor market movement across every major platform in real time, track the flow of money down to individual account level, and identify which customers are consistently profitable.
If you start winning at decent odds with any regularity, they don't wait to see how long it lasts.
A flag goes up, your account gets reviewed, and your maximum stake gets cut, sometimes within a week of a good run starting.
That's not coincidence or poor timing on your part.
It's a system running exactly as its designers intended.
Their pricing models aren't perfect, though.
They're built to generate margin across an enormous volume of bets, not to price every runner at every meeting with pinpoint accuracy.
At smaller mid-week meetings where public interest is lower, market prices shift with whatever money comes in first rather than from deep independent analysis.
That creates gaps in the pricing, small ones, brief ones, gaps that close quickly once money starts moving.
But consistent, repeatable gaps that the app scans for and is specifically built to find before they disappear.
That's the territory Bet Analyser App operates in.
I want to give you a proper picture of what those 25 years of losing looked like before the app changed everything.
It wasn't one single catastrophic failure.
It was a long accumulation of smaller ones.
A tipster service I followed for 4 months that started brilliantly, then quietly fell apart.
A staking system I tried that looked mathematically sound on paper and produced a steady bleed in practice.
A period where I decided I'd finally understood the market well enough to back my own selections exclusively, and lost £1,400 over 6 weeks before I stopped.
Each experience taught me something about what doesn't work.
But none of it taught me what would.
The racing journalists whose columns I read every morning had their moments but were ultimately as subject to their own blind spots as I was.
The systems I bought either worked for a few weeks and then stopped, or never worked at all.
And the tipsters I trusted, without exception, had one thing in common.
They were working from the same publicly available information as everyone else, with no systematic edge, no real data infrastructure behind their selections, and no way to consistently find the horses the market had mispriced.
What I didn't understand then, but understand completely now, is that the problem was never the horses.
The problem was the method.
THE MAN BEHIND THE APPMy name is Dan Wilson, a 51-year-old family man from Bristol.
I've had two obsessions running in parallel my entire adult life.
My family, and betting on the UK horses.
The horses got me into trouble more than once, never the kind that wrecked anything important, but the kind that builds a slow, compounding frustration over years of knowing you're putting in the work and still coming up short.
I started betting seriously in my late twenties.
I studied form the way other people study for exams, tracked trainers, noted jockeys in form, paid attention to going conditions, and kept detailed records of every bet I placed going back to 2007.
Those records told me the same uncomfortable story year after year.
Steadily, consistently, more money was leaving my account than was coming back into it.
Not dramatically, not in a way that threatened anything important.
But enough to know something was fundamentally wrong with my approach.
I tried tipster services, bought systems, followed well-known selectors and completely obscure ones.
I had good patches, weeks where everything clicked and I felt like I'd cracked it.
Then the run would end and the losses would quietly undo most of what I'd built.
I kept thinking I was one more piece of information, one better system, one smarter angle away from finally getting on top of it.
That feeling kept me going for a long time.
It also kept me losing.
18 YEARS AS A SOFTWARE SYSTEMS ARCHITECTBy profession, I'd spent 18 years working as a software systems architect.
My job was designing and building the data processing infrastructure that large organisations use to handle high volumes of complex information in real time, systems that ingested millions of records per day, flagged patterns automatically, and produced reliable outputs in seconds.
I was good at that work.
I understood how to take a messy, complicated data problem and engineer something reliable out of it.
But I kept those skills completely separate from my betting life.
Work was work, and the horses were a hobby I couldn't quite get under control.
Until one Tuesday afternoon in late 2023, sitting at my desk staring at a race card I'd already spent two hours on, something clicked.
I'd backed a horse the week before with complete confidence at 3/1 in the morning.
By post time it had drifted out to 9/2.
Then it won easily.
The market had been telling me something I'd ignored completely.
Money drifting away from a horse before a race means the sharper side of the market doesn't share your confidence.
But the raw data I'd never properly examined suggested that horse was actually better value at 9/2 than it had been at 3/1 first thing that morning.
I'd been watching racing as a form student for 25 years.
I should have been looking at it as a data engineer.
That thought planted itself and didn't shift.
I started pulling historical race data that same week.
Not newspaper tips, not form guides.
Raw data from multiple feeds covering thousands of races going back several seasons, every declared runner, every result, every going description, every price movement from overnight declaration through to race start.
The first thing that stopped me was the state of the data itself.
Inconsistent field names across different sources, trainer names formatted three different ways depending on which feed you pulled from, going descriptions that meant slightly different things at different tracks, course names that didn't match cleanly between providers.
Before a single predictive model could run, the entire dataset needed cleaning, standardising, and stitching together properly.
That step alone took weeks of evenings.
Cross-referencing records, patching gaps, building the data pipeline that everything else would depend on.
I didn't rush it.
I'd spent enough time in my professional life watching predictive systems fail because they were built on a bad foundation.
Feed a model corrupted or inconsistent data and it learns the wrong lessons, produces plausible-looking outputs that don't hold up when real money is on the line.
That's not a shortcut I was willing to take here.
SOME FINDINGS DEMOLISHED 20 YEARS OF ASSUMPTIONSOnce the foundation was solid, I started testing which variables actually predicted outcomes.
Not which ones sounded important to racing fans.
Which ones, tested objectively across thousands of historical races, consistently correlated with results.
Some findings were straightforward, others were counterintuitive, and a few of them demolished assumptions I'd been carrying for 20 years.
Factors the racing public treats as gospel turned out to carry almost no independent predictive weight when you isolated them properly from everything else.
A big-name jockey being on a roll.
A stable that had sent out a cluster of winners at a particular track recently.
A horse that looked overdue a win after a run of placed efforts.
Those things move markets because people believe in them.
Not because the data consistently backs them up.
And that matters for one specific reason.
When a market prices a horse based on factors the data says don't reliably predict outcomes, that price is wrong.
When a price is wrong and you know the correct probability, you have a real edge.
The whole app is built around finding exactly that.
HUNDREDS OF VARIABLES. EVERY RACE. EVERY MORNING.Here's what sets this apart from anything a human tipster can offer you.
The most experienced tipsters assess somewhere between 20 and 30 factors when forming a view on a race.
That's their ceiling, not because they're cutting corners, but because that's the upper limit of what the human brain can manage reliably before fatigue, impatience and personal bias start bleeding into the process.
My app assesses hundreds of weighted variables for every declared runner across every meeting on the day's full card.
Not just whether a horse has won at this course before, but at what going, over what distance, in what class of race, with which jockey, coming off what kind of recent run, carrying what weight, how the price has moved from the overnight show to the morning market.
Every variable, every runner, every race, every morning, before you've had breakfast.
The app also weights variables differently depending on the specific race context.
Draw position is highly significant in sprint handicaps at tight, turning tracks with large fields, particularly where course statistics show a strong bias toward a specific part of the draw.
In a 2-mile staying race on a wide, straight track, draw position barely registers.
Trainer form is a strong signal in maiden races and novice events where there's limited individual performance history to analyse.
In a competitive open handicap with 20-plus runners, it carries much less weight because that information is widely available and already priced in by the market.
Going preference matters enormously for horses with a strong predisposition to a specific surface, and almost not at all for others who handle most conditions equally well.
Market movement data, specifically how a price has shifted from its opening show through to the morning market, gets its own weighting that changes depending on the race type and the track.
The model learned these distinctions from thousands of historical races.
I didn't program them in as fixed rules.
That's the critical difference between a rule-based filter and a trained machine learning model.
Fixed rules crack at the edges of the dataset they were built for, or when conditions change in ways the rule-writer didn't anticipate.
A trained model adapts, because it's absorbed enough context to understand which signals actually matter in which situations.
THE APP LEARNS FROM EVERY SINGLE BETThe app also improves continuously.
Every morning I feed in the full results from the previous day.
Winning selections confirm the model's probability estimates were in the right range.
Losing selections get analysed to identify which variables were overweighted for that specific race context.
The model adjusts those weightings before the next morning's selections are produced.
Applied every day for 18 months, that compounding improvement is exactly why the 2026 numbers are running ahead of 2025.
A 25% CHANCE PRICED AT 9/2 IS WHERE THE MONEY LIVESThe core logic behind the selections is this.
I stopped trying to find horses I thought would win.
I started finding horses whose true probability of winning is meaningfully higher than what the bookmaker's published odds imply.
A horse with a 25% chance of winning should be priced around 3/1.
If the market has it at 9/2 or 5/1, that runner is mispriced, and there's a real edge worth acting on.
The horse doesn't have to win that specific race for the bet to be the right decision.
It just needs to win often enough, at prices that reflect the gap between the model's probability and the bookmaker's price, across a large enough sample to produce a consistent profit.
That's the exact logic bookmakers use when setting their own margins, extracting money by being right about probabilities more often than not across huge volumes of bets.
My app applies the same logic in reverse, finding the horses where the bookmaker's probability assumptions are wrong.
The selections don't win every time.
But they win often enough, and at prices big enough, to build a real profit month after month.
DAY ONE: TWO WINNING BETSThe first live morning was the 14th of January 2025.
Before that, the app had been running in shadow mode for 3 months, logging every selection it would have made without any real money involved.
The shadow results gave me confidence, but 3 months of paper trades and 3 months of real stakes are two very different propositions, and I knew it.
I placed real bets on the model's first 2 live selections before breakfast and went to work.
By 4 in the afternoon, both had won.
Day one profit: £290.
I didn't celebrate.
2 winners on day one proves nothing.
What matters is what a system does over hundreds of bets across many months.
But the feeling that afternoon was different to anything I'd experienced from betting before.
It wasn't the excitement of a lucky result.
It was more like watching something you'd engineered carefully finally work the way it was designed to.
By the end of January 2025, the app had flagged 21 selections, 13 of which had won.
A strike rate of 62% and a monthly profit of £4,210.
More in one month than I'd made from betting in any full year before building the app.
February was quieter on the bigger-priced winners but still came in at 57% and just under £3,700.
March 2025 stepped things up considerably.
26 winners from 40 selections, a 65% strike rate, and £5,940 in profit.
That was the month I pulled £5,000 out of my betting accounts, cleared the last of a credit card I'd been dragging around for years, and took my wife out for a dinner we'd been putting off far too long.
The momentum continued through spring and summer of 2025.
There were quieter months, weeks where the selections didn't land at the same rate.
Short-term variance is unavoidable in any probabilistic system.
But the model absorbed those patches the way a sound process should, running on the same criteria regardless of recent results, feeding the losing selections back in as fresh training data, and coming back sharper.
Not one month through the whole of 2025 finished at a loss.
2026 arrived and the results stepped up again.
January and February came in strongly.
March produced £6,460.
Then April 2026 delivered the best single month since the app went live.
23 winners from 33 selections, a 70% strike rate, and £7,080 in profit.
The model had processed another full year of live results on top of everything before it, and the improvement in selection quality was measurable in the numbers.
May kept that momentum going at £5,960.
NOT ONE LOSING MONTH ACROSS 12 MONTHSBy the time I reached mid-2026, I'd stopped being surprised by the monthly results.
Not because the numbers had become ordinary, but because the process behind them had become so reliable that a profitable month felt like a confirmation rather than a result.
The model had, by that point, processed over 350 live races.
Each one had fed back into the training data.
Each losing selection had adjusted a weighting somewhere in the model, marginally, but cumulatively in a way that was showing up clearly in the outputs.
The strike rate in the first 6 months of live running averaged around 59%.
In the 6 months from November 2025 to April 2026, it averaged 63%.
That 4% improvement translates directly into a meaningful difference in monthly profit at any stake level.
At £50 per bet, the difference between a 59% and a 63% strike rate across 30 selections in a month is roughly an extra £600 in your pocket.
That's the compounding effect of a model that keeps learning.
And it's still improving.
Here's the full 12-month picture.
| Month | Bets | Winners | Strike Rate | Monthly Profit |
|---|---|---|---|---|
| June 2025 | 28 | 15 | 54% | £3,620 |
| July 2025 | 34 | 21 | 62% | £5,840 |
| August 2025 | 32 | 21 | 66% | £6,380 |
| September 2025 | 27 | 14 | 52% | £4,120 |
| October 2025 | 35 | 23 | 66% | £6,720 |
| November 2025 | 30 | 17 | 57% | £5,280 |
| December 2025 | 25 | 13 | 52% | £3,850 |
| January 2026 | 29 | 17 | 59% | £4,760 |
| February 2026 | 28 | 15 | 54% | £4,340 |
| March 2026 | 34 | 22 | 65% | £6,460 |
| April 2026 | 33 | 23 | 70% | £7,080 |
| May 2026 | 32 | 21 | 66% | £5,960 |
| 12-Month Total | 367 | 222 | 61% | £64,410 |
Even the two quietest periods, September and December 2025, came in above £3,800.
The 2026 months are the strongest the app has produced since it went live.
That's what happens when a machine learning model absorbs a full year of live results and keeps refining its approach from them.
72-POINT RETURN IN THE SOFTEST MONTHLet's put those numbers into practical context for a moment.
At £50 flat stakes per selection, which is what the results table above is based on, the worst month in the last 12 (June 2025 at £3,620) still represents a 72-point return on the month's total investment.
The best month, April 2026 at £7,080, represents a 139-point return.
The average across the 12 months comes to just over £5,367 per month at those stakes.
You don't have to bet at £50 a selection.
The same percentage returns apply at any stake level.
At £10 per bet the numbers scale down proportionally.
At £100 per bet they scale up.
The point is that the edge the app produces is consistent and measurable, not dependent on backing long shots or building accumulators that need to land perfectly.
These are straightforward win-only bets on UK horse races, placed one at a time, with flat stakes.
The kind of betting that doesn't require a complicated staking plan or a large bank to get started with.
There are dozens of tipping services running right now, and most of them won't be here in 18 months.
Not always because of any bad intent, but because the model they're built on has a ceiling, and most reach it quickly.
The first problem is the information they're working from.
Most tipsters rely on the same publicly available form data every punter can access for free, the same newspaper analysis, the same stable reports, the same track conditions available to anyone.
That information is already reflected in the price before the tipster has finished reading it.
Real pricing inefficiencies live in the intersection of less obvious data points that nobody's looking at in combination.
A person working manually simply can't process enough variables simultaneously to find those combinations with any consistency.
The second problem is consistency.
A tipster mid-winning-run feels sharp and confident, backing selections they might have hesitated over a month earlier.
The same tipster after a bad fortnight starts second-guessing their own process, adding conditions that conveniently explain recent losses, skipping selections they should follow.
They call it refining the method.
It's doubt getting into the decision-making.
My app doesn't have bad weeks.
It doesn't wake up on a grey Thursday morning and decide it doesn't fancy the card.
The same process runs at the same standard every single morning without exception, regardless of what happened the day before.
The third problem is the complete absence of a feedback loop.
A standard tipster sends a selection out, it loses, and they move straight on to the next one.
Nothing about that lost bet feeds back into how the next selection gets made.
Every losing selection in my system gets analysed before the following morning.
The model identifies which variables it overweighted for that race context and adjusts the weighting accordingly before it runs again the next day.
Over time, the losing selections actually make the whole system sharper.
No tipster working manually can replicate that, regardless of how experienced they are.
The fourth problem is scale.
The moment a successful tipping service grows its subscriber base significantly, the collective weight of money following the same selections starts to move the market price before everyone can get their bets placed.
The value that made the selection worth taking starts to erode from within.
That's a structural ceiling every successful tipster eventually runs into.
Keeping membership deliberately small is the only way to protect the edge for everyone inside the group.
Which is why there are only 75 places in Bet Analyser App, and it'll stay that way.
People often ask me whether the bookmakers will eventually work out what the app is doing and close the gap.
It's a fair question.
The bookmakers' own AI pricing systems do improve over time, and they will keep improving.
But here's the thing.
My model improves too, and it improves specifically in response to the same market conditions the bookmakers are creating.
Every morning I feed in the results from the previous day, which includes the prices that were available, how they moved, and how the selections performed against those prices.
The model is learning from live market conditions in real time, not from static historical data.
That's why the gap between the app's probability estimates and the bookmakers' published prices hasn't narrowed since the system went live.
If anything, the results table above shows it's widened slightly as the model has matured.
There's also a structural reason why this edge is more durable than most people assume.
The bookmakers price thousands of races per day across multiple sports.
Their pricing resources are spread across an enormous range of markets.
My app focuses exclusively on UK horse racing.
That's a very narrow specialisation compared to what the bookmakers' systems have to cover.
It means the model can go deeper into racing-specific data patterns than any general-purpose pricing algorithm the bookmakers operate.
The bookmakers are building tools designed to manage exposure across millions of customers and thousands of events.
Bet Analyser App is built to find 2 or 3 specific horses per day where their margin assumptions are wrong.
Those are completely different objectives, and that's why the two systems aren't really in direct competition with each other in the way people imagine.
The bookmakers want volume.
I want precision.
And right now, in June 2026, that precision is producing results you can see laid out in the table above.
One more thing worth understanding before I explain how to join.
This isn't a system that works in a bull market and falls apart the moment conditions change.
The 12-month results table covers a wide range of racing conditions, different courses, different seasons, summer flat racing through to winter jumps meetings, going conditions from firm to heavy.
The model has produced a profit in every one of those months, not because the selections are only coming from the most favourable race types, but because the app adapts its variable weighting to the specific conditions of each race.
It doesn't have a strong month in summer and a weak one in winter.
It processes the available data for whatever races are on the card that morning and finds the best value within them.
That consistency across conditions is what makes the 12-month track record actually meaningful rather than a run of results from a narrow window of good conditions.
IN YOUR INBOX BEFORE 8AM. EVERY MORNING.Here's how this works for you on a practical, day-to-day basis.
Every morning, before the first race of the day goes off, the app runs through the full declared card.
Every runner at every meeting gets assessed against hundreds of weighted variables.
The selections where the model's calculated win probability sits meaningfully above the available market price are flagged and packaged into an email.
That email lands in your inbox before 8am.
You open it, place the bets at your usual bookmaker, and get on with your day.
The whole thing takes around 5 minutes on your side.
You can do it from your phone before you leave the house.
Then you check the results later when you've got a minute.
That's the entire daily commitment.
No form guides to read, no statistics to decode, no complicated system to learn and then forget.
Bet Analyser App has done the analysis hours before you wake up.
It won't require you to have...
Technical ability of any kind (all you do is copy the selections and place the bets).
Significant free time (5 minutes in the morning is the full commitment).
Prior racing knowledge or experience (the model has assessed everything there is to assess).
You can be any age over 18, and you can start today.
There's no learning curve and no special equipment beyond a phone and a betting account.
Here's what 3 more members told me after following the bets through the spring 2026 trial.
SPRING 2026 MEMBER RESULTS“I kept a spreadsheet of every bet across the full 30 days because I wanted a proper breakdown rather than a rough impression. Final count: 61% strike rate across 28 selections and £2,240 in profit, more than I’d made from betting in the previous six months combined.”
Alex Fielding, Luton
“I joined on a Tuesday and had my membership fee covered before the end of the first week. By the end of the month I was £1,980 up, and the whole thing added about 10 minutes to my morning routine.”
Claire Sutton, Blackburn
“Close to 40 years of placing bets on the horses, and this is the first service I’ve followed that actually delivers what it describes on the page. The 2026 results have been exceptional, and I’m up £2,410 this month without changing a single thing about how I normally operate.”
Raymond Harper, Plymouth
| Date | Horse | Course | Odds | Result | P/L | |
|---|---|---|---|---|---|---|
| 19 May | Moreedd | Nottingham | 5/1 | WON | +£250.00 | |
| 19 May | Volendam | Nottingham | 6/1 | LOST | -£50.00 | |
| 20 May | Rogue Allegience | Yarmouth | 5/1 | LOST | -£50.00 | |
| 20 May | Havana Lightning | Yarmouth | 3/1 | WON | +£150.00 | |
| 21 May | Betsen | Chepstow | 3/1 | WON | +£150.00 | |
| 21 May | Autumn Angel | Chepstow | 9/2 | WON | +£225.00 | |
| 22 May | Sea Venture | Haydock | 8/1 | WON | +£400.00 | |
| 22 May | Orionis | Goodwood | 7/2 | WON | +£175.00 | |
| 23 May | Dreamasar | Haydock | 8/1 | WON | +£400.00 | |
| 23 May | Kientzheim | Cartmel | 13/2 | LOST | -£50.00 | |
| 24 May | Footloose Man | Fontwell | 6/1 | WON | +£300.00 | |
| 24 May | Dunkerque | Kelso | 5/2 | WON | +£125.00 | |
| 25 May | Centurion's Sister | Cartmel | 9/1 | LOST | -£50.00 | |
| 25 May | The Wise Traveller | Huntingdon | 11/4 | WON | +£137.50 | |
| 26 May | Cougar Force | Lingfield | 8/1 | LOST | -£50.00 | |
| 26 May | Libertango | Leicester | 4/1 | WON | +£200.00 | |
| 27 May | Sale Shark | Hamilton | 13/8 | WON | +£81.25 | |
| 27 May | Ludo's Landing | Hamilton | 6/1 | LOST | -£50.00 | |
| 28 May | Spioradalta | Ripon | 5/1 | WON | +£250.00 | |
| 28 May | Something | Ripon | 4/1 | WON | +£200.00 | |
| Total Winners: 13 Total Losers: 7 | Total Profit: £2,693.75 | |||||
For the first time since I started running Bet Analyser App privately with a small group, I'm opening 75 places to the public.
75 spots, and not one more than that.
The reason for that number is straightforward.
When too many people back the same selections at the same time, the collective weight of money going on those horses starts to shift the market price before everyone can get their bets placed.
The value gap that made the selection worth taking starts to close before it's been fully exploited.
By keeping membership at 75, the odds available when I send the selections out each morning are still there when you log in to place your bets.
It's the only way to protect the edge that produced the 12 months of results in the table above.
If you're reading this right now, there's a decent chance one of those spots is still available.
But I'd strongly suggest not relying on it being there if you come back later.
ONE PAYMENT OF £20. LIFETIME ACCESS.It's not my intention to make a profit selling memberships.
I make my money from the bets, the same as you will.
So the price for lifetime access to Bet Analyser App is a one-time payment of £20.
One single payment of £20 today, and nothing more to pay.
No recurring monthly fees.
No hidden charges.
30-Day Money-Back Guarantee
And if you're on the fence about whether this is right for you, here's what backs it up.
Join today, follow my bets for up to 30 days.
If for any reason at all you're not satisfied within that window, send me one message and I'll return every penny of your £20 immediately.
No conditions, no process to go through, no questions asked.
You can follow the bets at £1 a time if you want.
You can write them down and track them on paper without any money on the line at all, just to see the strike rate playing out in real time.
Whatever it takes to be certain this is real before you commit to anything.
You have nothing to lose and everything to gain.
Thank you for taking the time to read through this.
The 75 spots will fill quickly.
If you're here now, take your place before someone else does.
I'll see you inside.
Dan Wilson
BEFORE YOU GOThe app runs every morning regardless of what's on the card.
Whether it's a busy Saturday with 7 meetings and 50-plus races, or a quiet Tuesday with 3 meetings and a thin card, the model processes everything available and delivers selections only where the value gap is wide enough to act on.
On days where nothing clears the threshold, no selection goes out and I'll say so clearly in the email.
Quality over volume is the principle the app operates on, and it's the reason the strike rate in the table above has stayed above 50% in every single month.
P.S. Bet Analyser App flagged 3 selections yesterday. 2 horses won. The daily email goes out before 8am every day, and if you join now, today's selections will be waiting for you in the members area.
P.P.S. In June 2026, the app is running at its highest accuracy since it went live. The table above shows you exactly what the last 12 months have produced. There's no better time to start than now, and with a 30-day money-back guarantee covering your entire membership fee, there's no risk in finding out.